#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')

library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(plotlyutils)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(Rtsne)
library(ClusterR)
library(DESeq2)
library(expss)
library(knitr)

Load preprocessed dataset (preprocessing code in 20_03_30_data_preprocessing.Rmd)

# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame

# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>% 
              mutate('ID'=as.character(ensembl_gene_id)) %>% 
              dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
              mutate('Neuronal'=1)

# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_with_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]

# Update DE_info with SFARI and Neuronal information
DE_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>% 
  mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
  distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
  mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
  mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`), significant=padj<0.05 & !is.na(padj))


SFARI_colour_hue = function(r) {
  pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}

SFARI Gene list

cat(paste0('There are ', length(unique(SFARI_genes$`gene-symbol`)), ' genes with a SFARI score'))
## There are 979 genes with a SFARI score

The results from this section don’t change depending on the brain region analised, so I’m not going to repeat them here. These can be found in the folder where all the brain regions were analysed together


Exploratory Analysis

As in the previous section, the results from this section don’t change depending on the brain region analised, so I’m not going to repeat them here. These can be found in the folder where all the brain regions were analysed together


Gene Expression


Normalised data

  • The higher the SFARI score, the higher the mean expression of the gene: This pattern is quite strong and it doesn’t have any biological interpretation, so it’s probably bias in the SFARI score assignment

  • The higher the SFARI score, the higher the standard deviation: This pattern is not as strong, but it is weird because the data was originally heteroscedastic with a positive relation between mean and variance, but this was supposed to have been corrected with the vst transformation

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>% 
            left_join(DE_info, by='ID')

p1 = ggplotly(plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              theme(legend.position='none'))

p2 = ggplotly(plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              ggtitle('Mean Expression (left) and SD (right) by SFARI score') + 
              theme(legend.position='none'))

subplot(p1, p2, nrows=1)
rm(plot_data, p1, p2)


Raw data

Just to corroborate that the relation between sd and SFARI score used to be in the opposite direction before the normalisation: The higher the SFARI score the higher the mean expression and the higher the standard deviation

*There are a lot of outliers, but the plot is interactive so you can zoom in

# Save preprocessed results
datExpr_prep = datExpr
datMeta_prep = datMeta
DE_info_prep = DE_info

load('./../Data/filtered_raw_data.RData')

plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr), 'SDExpr'=apply(datExpr,1,sd)) %>% 
            left_join(DE_info, by='ID')

p1 = ggplotly(plot_data %>% ggplot(aes(gene.score, MeanExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              theme(legend.position='none'))

p2 = ggplotly(plot_data %>% ggplot(aes(gene.score, SDExpr, fill=gene.score)) + geom_boxplot() + 
              scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + theme_minimal() +
              ggtitle('Mean Expression (left) and SD (right) by SFARI score') + 
              theme(legend.position='none'))

subplot(p1, p2, nrows=1)
rm(plot_data, p1, p2)

Return to normalised version of the data

# Save preprocessed results
datExpr = datExpr_prep
datMeta = datMeta_prep
DE_info = DE_info_prep

rm(datExpr_prep, datMeta_prep, DE_info_prep)


Log Fold Change

There seems to be a negative relation between SFARI score and log fold change when it would be expected to be either positively correlated or independent from each other (this last one because there are other factors that determine if a gene is releated to Autism apart from differences in gene expression)

Wikipedia mentions the likely explanation for this: “A disadvantage and serious risk of using fold change in this setting is that it is biased and may misclassify differentially expressed genes with large differences (B − A) but small ratios (B/A), leading to poor identification of changes at high expression levels”.

Based on this, since we saw there is a strong relation between SFARI score and mean expression, the bias in log fold change affects mainly genes with high SFARI scores, which would be the ones we are most interested in.

This pattern is stronger in the Gandal dataset

ggplotly(DE_info %>% ggplot(aes(x=gene.score, y=abs(log2FoldChange), fill=gene.score)) + 
         geom_boxplot() + scale_fill_manual(values=SFARI_colour_hue(r=c(1:6,8,7))) + 
         theme_minimal() + theme(legend.position='none'))


Effects of modifying filtering threshold by SFARI score

The higher the percentage of genes that get filtered by differential expression. This pattern is not as clear as with Gandal’s dataset

lfc_list = seq(1, 1.15, 0.005)

all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_info)))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_info$Neuronal)))
lfc_counts_all = DE_info %>% group_by(`gene-score`) %>% tally %>%
                 mutate('group'=as.factor(`gene-score`), 'n'=as.character(n)) %>%
                 dplyr::select(group, n) %>%
                 bind_rows(Neuronal_counts, all_counts) %>%
                 mutate('lfc'=-1) %>%  dplyr::select(lfc, group, n)

for(lfc in lfc_list){
  
  # Recalculate DE_info with the new threshold (p-values change)
  DE_genes = results(dds, lfcThreshold=log2(lfc), altHypothesis='greaterAbs') %>% data.frame
  
  DE_genes = DE_genes %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>% 
             mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
             distinct(ID, .keep_all = TRUE) %>% left_join(GO_neuronal, by='ID') %>%
             mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
             mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`))
  
  DE_genes = DE_genes %>% filter(padj<0.05 & abs(log2FoldChange)>log2(lfc))

  
  # Calculate counts by groups
  all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
  Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$Neuronal)))
  lfc_counts = DE_genes %>% group_by(`gene-score`) %>% tally %>%
               mutate('group'=`gene-score`, 'n'=as.character(n)) %>%
               bind_rows(Neuronal_counts, all_counts) %>%
               mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
  
  
  # Update lfc_counts_all
  lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}

# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>% 
  left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)

# Calculate percentage of each group remaining
tot_counts = DE_info %>% group_by(`gene-score`) %>% tally() %>% filter(`gene-score`!='None') %>%
             mutate('group'=`gene-score`, 'tot'=n) %>% dplyr::select(group, tot) %>%
             bind_rows(data.frame('group'='Neuronal', 'tot'=sum(DE_info$Neuronal)),
                       data.frame('group'='All', 'tot'=nrow(DE_info)))

lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1, group!='None') %>% 
                 left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))


# Plot change of number of genes
ggplotly(lfc_counts_all %>% ggplot(aes(lfc, perc, color=group)) + geom_point(aes(id=n)) + geom_line() + 
         scale_color_manual(values=SFARI_colour_hue(r=1:8)) + ylab('% of remaining genes') +  xlab('Fold Change') + 
         ggtitle('Effect of filtering thresholds by SFARI score') + theme_minimal())
rm(lfc_list, all_counts, Neuronal_counts, lfc_counts_all, lfc, lfc_counts, lfc_counts_all, tot_counts, lfc_counts_all)
cat(paste0('There are ', sum(DE_info$padj<0.05 & DE_info$`gene-score` != 'None' & !is.na(DE_info$padj)),
           ' SFARI genes that are differentially expressed'))
## There are 87 SFARI genes that are differentially expressed
kable(DE_info %>% filter(padj<0.05 & `gene-score` %in% c(1,2,3) & !is.na(padj)) %>% 
      dplyr::select(ID, `gene-symbol`, log2FoldChange, padj, `gene-score`, Neuronal) %>% arrange(`gene-score`,padj),
      caption = 'Top SFARI scores that are DE')
Top SFARI scores that are DE
ID gene-symbol log2FoldChange padj gene-score Neuronal
ENSG00000251322 SHANK3 -0.4342702 0.0030963 1 1
ENSG00000118058 KMT2A 0.4183848 0.0040453 1 0
ENSG00000197283 SYNGAP1 -0.3340702 0.0249260 1 1
ENSG00000168137 SETD5 0.2987199 0.0252926 1 0
ENSG00000157103 SLC6A1 -0.7070327 0.0001111 2 1
ENSG00000174469 CNTNAP2 -0.2985477 0.0110806 2 1
ENSG00000038382 TRIO -0.2589181 0.0349991 2 0
ENSG00000177030 DEAF1 -0.2747991 0.0427330 2 0
ENSG00000141027 NCOR1 0.3393049 0.0452036 2 0
ENSG00000079432 CIC -0.2131121 0.0469210 2 0
ENSG00000215301 DDX3X -0.2608556 0.0486155 2 0
ENSG00000136854 STXBP1 -0.3710927 0.0000790 3 1
ENSG00000181722 ZBTB20 0.8772604 0.0011067 3 0
ENSG00000196628 TCF4 0.4099807 0.0036609 3 1
ENSG00000135439 AGAP2 -0.5040011 0.0040737 3 1
ENSG00000157087 ATP2B2 -0.4749078 0.0057662 3 1
ENSG00000124140 SLC12A5 -0.3447022 0.0059061 3 1
ENSG00000196361 ELAVL3 0.3181172 0.0158828 3 0
ENSG00000146247 PHIP 0.4637417 0.0202719 3 0
ENSG00000161681 SHANK1 -0.4667923 0.0282489 3 1
ENSG00000114062 UBE3A 0.3068265 0.0356651 3 0
ENSG00000074054 CLASP1 -0.3196193 0.0356693 3 0
ENSG00000100241 SBF1 -0.2378509 0.0405028 3 0
ENSG00000108557 RAI1 -0.3303705 0.0476327 3 0

Session info

sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] knitr_1.28                  expss_0.10.2               
##  [3] DESeq2_1.24.0               SummarizedExperiment_1.14.1
##  [5] DelayedArray_0.10.0         BiocParallel_1.18.1        
##  [7] matrixStats_0.56.0          Biobase_2.44.0             
##  [9] GenomicRanges_1.36.1        GenomeInfoDb_1.20.0        
## [11] IRanges_2.18.3              S4Vectors_0.22.1           
## [13] BiocGenerics_0.30.0         ClusterR_1.2.1             
## [15] gtools_3.8.1                Rtsne_0.15                 
## [17] GGally_1.5.0                gridExtra_2.3              
## [19] viridis_0.5.1               viridisLite_0.3.0          
## [21] RColorBrewer_1.1-2          plotlyutils_0.0.0.9000     
## [23] plotly_4.9.2                glue_1.3.2                 
## [25] reshape2_1.4.3              forcats_0.5.0              
## [27] stringr_1.4.0               dplyr_0.8.5                
## [29] purrr_0.3.3                 readr_1.3.1                
## [31] tidyr_1.0.2                 tibble_3.0.0               
## [33] ggplot2_3.3.0               tidyverse_1.3.0            
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1       ellipsis_0.3.0         htmlTable_1.13.3      
##  [4] XVector_0.24.0         base64enc_0.1-3        fs_1.3.2              
##  [7] rstudioapi_0.11        bit64_0.9-7            AnnotationDbi_1.46.1  
## [10] fansi_0.4.1            lubridate_1.7.4        xml2_1.2.5            
## [13] splines_3.6.3          geneplotter_1.62.0     Formula_1.2-3         
## [16] jsonlite_1.6.1         annotate_1.62.0        broom_0.5.5           
## [19] cluster_2.1.0          dbplyr_1.4.2           png_0.1-7             
## [22] compiler_3.6.3         httr_1.4.1             backports_1.1.5       
## [25] assertthat_0.2.1       Matrix_1.2-18          lazyeval_0.2.2        
## [28] cli_2.0.2              acepack_1.4.1          htmltools_0.4.0       
## [31] tools_3.6.3            gmp_0.5-13.6           gtable_0.3.0          
## [34] GenomeInfoDbData_1.2.1 Rcpp_1.0.4             cellranger_1.1.0      
## [37] vctrs_0.2.4            nlme_3.1-144           crosstalk_1.1.0.1     
## [40] xfun_0.12              rvest_0.3.5            lifecycle_0.2.0       
## [43] XML_3.99-0.3           zlibbioc_1.30.0        scales_1.1.0          
## [46] hms_0.5.3              yaml_2.2.1             memoise_1.1.0         
## [49] rpart_4.1-15           RSQLite_2.2.0          reshape_0.8.8         
## [52] latticeExtra_0.6-29    stringi_1.4.6          highr_0.8             
## [55] genefilter_1.66.0      checkmate_2.0.0        rlang_0.4.5           
## [58] pkgconfig_2.0.3        bitops_1.0-6           evaluate_0.14         
## [61] lattice_0.20-40        labeling_0.3           htmlwidgets_1.5.1     
## [64] bit_1.1-15.2           tidyselect_1.0.0       plyr_1.8.6            
## [67] magrittr_1.5           R6_2.4.1               generics_0.0.2        
## [70] Hmisc_4.4-0            DBI_1.1.0              pillar_1.4.3          
## [73] haven_2.2.0            foreign_0.8-75         withr_2.1.2           
## [76] survival_3.1-11        RCurl_1.98-1.1         nnet_7.3-13           
## [79] modelr_0.1.6           crayon_1.3.4           rmarkdown_2.1         
## [82] jpeg_0.1-8.1           locfit_1.5-9.4         grid_3.6.3            
## [85] readxl_1.3.1           data.table_1.12.8      blob_1.2.1            
## [88] reprex_0.3.0           digest_0.6.25          xtable_1.8-4          
## [91] munsell_0.5.0